Download Advances in Neural Networks – ISNN 2012: 9th International by Dazhong Ma, Jinhai Liu, Zhanshan Wang (auth.), Jun Wang, PDF

By Dazhong Ma, Jinhai Liu, Zhanshan Wang (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)

The two-volume set LNCS 7367 and 7368 constitutes the refereed complaints of the ninth foreign Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised complete papers offered have been rigorously reviewed and chosen from a variety of submissions. The contributions are dependent in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; trend attractiveness; imaginative and prescient; photograph processing; details processing; neurocontrol; and novel applications.

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Extra resources for Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part II

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It divides all input dimensions into several sub-dimensions, each of which corresponds to an input feature. After this step, instead of learning input features altogether as an input vector in training, ITID learns inputs through their corresponding sub-networks one after another and the structure of neural networks gradually grows with an increasing input dimension as shown in Figure 1. During training, information obtained by a new sub-network is merged together with the information obtained by the old network.

2 IAL Base on Neural Networks Based on some predictive methods like neural networks, IAL has exhibits its feasibility in solving multi-dimensional classification problems in a number of previous studies. ITID [13], a representative of neural IAL based on ILIA [7], is shown applicable for classification. It is different from conventional approaches which trains all features in one batch. It divides all input dimensions into several sub-dimensions, each of which corresponds to an input feature. After this step, instead of learning input features altogether as an input vector in training, ITID learns inputs through their corresponding sub-networks one after another and the structure of neural networks gradually grows with an increasing input dimension as shown in Figure 1.

G. SVM classification [6-7], neural network [8], discriminant analysis [9]. -B. Huang et al [10-11]. Extreme learning machine has been effectively used in regression and classification problems. Though ELM tends to provide better generalization performance at a fast learning speed and relative simplicity of use [12], ELM algorithm may have uncertainty in different trials of prediction due to the stochastic initialization of input weights and bias, which would make the classification of the raw data unreliable.

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